Silvia Tellmann

Evaluation of Global Ozone Monitoring Experiment (GOME) ozone profiles from nine different algorithms

An evaluation is made of ozone profiles retrieved from measurements of the nadir-viewing Global Ozone Monitoring Experiment (GOME) instrument. Currently four different approaches are used to retrieve ozone profile information from GOME measurements, which differ in the use of external information and a priori constraints. In total nine different algorithms will be evaluated exploiting the Optimal Estimation (Royal Netherlands Meteorological Institute, Rutherford Appleton Laboratory, University of Bremen, National Oceanic and Atmospheric Administration, Smithsonian Astrophysical Observatory), Phillips-Tikhonov Regularization (Space Research Organization Netherlands), Neural Network (Center for Solar Energy and Hydrogen Research, Tor Vergata University), and Data Assimilation (German Aerospace Center) approaches. Analysis tools are used to interpret data sets that provide averaging kernels. In the interpretation of these data, the focus is on the vertical resolution, the indicative altitude of the retrieved value, and the fraction of a priori information. The evaluation is completed with a comparison of the results to lidar data from the NDSC (Network for Detection of Stratospheric Change) stations in Andoya (Norway), Observatoire Haute Provence (France), Mauna Loa (USA), Lauder (New Zealand) and Dumont d’Urville (Antarctic) for the years 1997–1999. In total the comparison involves nearly 1000 ozone profiles, and allows the analysis of GOME data measured in different global regions and hence observational circumstances. The main conclusion of this paper is that unambiguous information on the ozone profile can at best be retrieved in the altitude range 15–48 km with a vertical resolution of 10 to 15 km, precision of 5–10%, and a bias up to 5% or 20% depending on the success of recalibration of the input spectra. The sensitivity of retrievals to ozone at lower altitudes varies from scheme to scheme and includes significant influence from a priori assumptions.

Ozone Profile Retrieval from Broadband Nadir UV/Visible Satellite Spectra: How Accurate is the Tropospheric Profile?

With the new series of nadir viewing UV/visible satellite instruments SCIAMACHY/ENVISAT, GOME-2 and GOME-3 aboard METOP-1 and 2, succeeding the current and extremely successful GOME/ERS-2 mission, continuation of global ozone profile measurements in the next two decades will be possible. In the continuous UV/visible spectral range of these instruments (240–600 nm) the absorption of ozone and the multiple scattering cross-section varies several orders of magnitude, which makes it possible to derive, in addition to column abundance, height resolved ozone distribution from the respective nadir spectra using appropriate optimal estimation inversion schemes (Hoogen et al. 1999a, 1999b). However, the ozone information content in the UV/visible spectrum is dominated by lower stratospheric ozone making the tropospheric retrieval a quite challenging task. This project aims at improving the determination of tropospheric ozone by adding additional geophysical information contained in the spectra into the profile retrieval. In order to distinguish the profile retrieval from the total column retrieval, the advanced optimal estimation scheme is referred to as the full retrieval method (FURM).

Neural Network Ozone Profile Retrieval System for GOME Spectra (NNORSY-GOME)

A new approach for retrieving ozone profiles from ERS2-GOME spectral data has been developed, which relies on feed-forward neural networks to perform the data inversion. By using GOME spectral data from selected wavelength regions, instrument (e.g. scan angle), geolocation and UKMO temperature profile data as input, neural networks have been trained to determine the ozone profile from 1–60 km geopotential height in a one-step inversion scheme. In order to train neural networks, an extensive database of collocated GOME and ozone profile measurements is necessary. Ozone profiles from sondes collected by the World Ozone and Ultraviolet Data Centre (WOUDC), as well as HALOE, SAGE II and POAM III limb measurements have been utilised for this purpose, constituting about 70000 training collocations. While training takes a certain amount of time, it is only needed once, the actual retrieval process is by a factor of 103 to 105 faster than classical methods.

GOME satellite detection of ozone over a snow/ice covered surface in the presence of broken clouds

In the presence of broken clouds over snow or ice covered surfaces it is difficult for UV/VIS spaceborne sensors to distinguish between clouds and high reflecting surfaces. These uncertainties lead to problems in the ozone retrieval. A new approach presented here characterizes the inhomogeneous pixel area (with broken clouds) by deriving an effective albedo and cloud-top-height. These parameters are determined by minimizing the difference between GOME measured sun-normalized radiances and corresponding model spectra in the spectral range of O4 absorption near 370 nm. Extrapolation of this effective albedo to short wavelengths enables to improve the ozone retrieval. Besides this new retrieval method some new calibration corrections are made for the GOME spectra which take into account instrumental effects from dark current or straylight and the degradation from GOME.